Characterization of the adaptive mechanisms of grapevine rootstocks to iron deficiency induced by lime stress

The classical pharmacological model assumes that the effect of a drug is proportional to the fraction of receptors occupied by the drug. In the simplest circumstances, the relationship between dose of a drug and response, when plotted on a logarithmic scale for drug concentration, is described by a sigmoidal curve. It presumes the existence of a threshold dose, below which no biological effect appears, and a maximal response in the form of a plateau, when a further increase in the dose of drug has no effect.

Grapevine is one of the most extensively cultivated fruit crops, playing a crucial role in the economies of many grape-growing regions around the world.

Protecting table grape vineyards with white hail‐nets is a common practice in Southern Italy. Hail‐nets result in shading effects of 10‐20 %, depending on their density

The oenological potential of grapes at harvest depends on a combination of the major components of Terroir: the climate, the soil, the plant material, the training system and the crop management.

Grapevine yield and fruit quality are two major drivers of input allocation and, ultimately, revenue for grape producers. Because yield and fruit quality vary substantially from year-to-year and within a single block, opportunities exist for optimization via precision management activities that could lead to more profitable and sustainable grape production. Here, we review recent advances in the techniques and technology used to measure, estimate, and forecast grapevine yield and fruit quality. First, we discuss direct “measurement” of yield and quality (i.e. ground-truth data generation), with an emphasis on potential for scalability and automation. Second, we discuss technology and techniques that do not directly measure yield and quality, but use correlated measurements for their estimation.